Interactive visualizations

So far, we’ve been creating static plots in R. Now let’s move to interactive visuals.

We will use two packages: plotly and leaflet.

# install.packages(c("plotly", leaflet"))
library(plotly)
library(leaflet)
library(tidyverse)

plotly scatter plot

plotly is a software that provides open-source APIs for creating interactive visualizations. It is available in R, python, Matlab, and Javascript.

With the package loaded, we can convert ggplot objects easily into interactive graphics.

Let’s start from a static plot.

data(iris)
p1 <- ggplot(data =iris,aes(x =Sepal.Width,y =Petal.Width,color =Species)) +
  geom_point()
p1

Convert it into interactive visual.

ggplotly(p1)

We can also use plotly’s own function, plot_ly() to build interactive graphics.

plot_ly(
  data = iris,
  x = ~Sepal.Width, y = ~Petal.Width, color = ~Species,
  type = "scatter", # Specify the type of plot to create
  mode = "markers" # Determine the drawing mode for the scatter (point markers)
) %>%
  layout(
    title = "Iris data set visualization",
    xaxis = list(title = "Sepal width", ticksuffix = "cm"),
    yaxis = list(title = "Petal width", ticksuffix = "cm")
  )

Here is a 3D scatter plot by plot_ly().

plot_ly(
  data = iris, 
  x= ~Sepal.Length, y= ~Sepal.Width, z= ~Petal.Length, color = ~Species,
  type = "scatter3d",
  mode = "markers")

plotly time series plot

For visualization of multiple time series, it is useful to include annotations or hover tools.

Let’s create a static plot on psavert, personal savings rate, and unemploy, the number of unemployed in thousands.

data(economics_long)
economics_long %>%
  filter(variable %in% c("psavert", "uempmed")) %>%
  ggplot(aes(x = date, y = value, color = variable)) +
  geom_line(lwd = 1) +
  theme_minimal()

An interactive plot with hover tools:

df <- economics_long %>%
  filter(variable %in% c("psavert", "uempmed"))
plot_ly(
  data = df, 
  x= ~date, y= ~value, color = ~variable,
  type = "scatter",
  mode = "lines") %>%
  # Unified hovermode
  layout(hovermode = "x unified")

The leaflet package

leaflet is an open-source JavaScript library for interactive maps.

  • We can use it through the leaflet package in R.

The following code pin-points a single location in Singapore.

  • The addMarkers calls out point(s) on the map.
leaflet() %>%
  addTiles() %>%
  addMarkers(lng = 103.8238, lat = 1.2540, popup = "Universal Studio Singapore")

We can specify multiple pairs of latitude/longitude coordinates.

# Load data
data(quakes)
# Show the first 20 lines on the map
leaflet(quakes[1:20, ]) %>%
  addTiles() %>%
  addMarkers(lng = ~long, lat = ~lat, 
             popup = ~paste0("Magnitude: ", mag))

We can replace the location pins as circle markers via addCircleMarkers().

# Show the first 20 lines on the map
leaflet(quakes[1:20, ]) %>%
  addTiles() %>%
  addCircleMarkers(lng = ~long, lat = ~lat,
                   popup = ~paste0("Magnitude: ", mag),
                   radius = ~mag, stroke = FALSE, fillOpacity = 0.8)

Workshop: Real-time Carpark Availability

Let’s visualize the carpark availability data from LTA.

library(httr); library(jsonlite)
# Construct the resource URL
resource_url <- "http://datamall2.mytransport.sg/ltaodataservice/CarParkAvailabilityv2"
# Make the GET() request
res <- GET(resource_url, 
           add_headers(AccountKey = Sys.getenv("LTA_KEY"),
                       accept = "application/json"))
# Parse the returned data
res_content <- content(res, as = "text")
res_list <- fromJSON(res_content, flatten = TRUE)
# Extract information from sub-list
df_carpark <- as_tibble(res_list$value)
head(df_carpark)
CarParkID Area Development Location AvailableLots LotType Agency
1 Marina Suntec City 1.29375 103.85718 595 C LTA
2 Marina Marina Square 1.29115 103.85728 852 C LTA
3 Marina Raffles City 1.29382 103.85319 141 C LTA
4 Marina The Esplanade 1.29011 103.85561 582 C LTA
5 Marina Millenia Singapore 1.29251 103.86009 367 C LTA
6 Marina Singapore Flyer 1.28944 103.86311 227 C LTA 
# Extract information from sublist
df_carpark <- as_tibble(res_list$value) %>%
  separate(Location, into = c("lat", "long"), sep = " ", convert = TRUE)

leaflet(df_carpark) %>%
  addTiles() %>%
  addCircleMarkers(lng = ~long, lat = ~lat,
             popup = ~paste0("Available slots: ", AvailableLots),
             radius = ~AvailableLots/100, stroke = FALSE, fillOpacity = 0.5)